Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/cdeistopened/skill-stack/transcript-polishernpx skills add cdeistopened/skill-stack --skill transcript-polishergit clone --depth 1 https://github.com/cdeistopened/skill-stackWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00060 | $0.01977 |
| Opus 5 | $0.00030 | $0.00988 |
| Sonnet 5 | $0.00012 | $0.00395 |
| Haiku 4.5 | $0.00006 | $0.00198 |
Grade A, and why
transcript-polisher scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 283 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Transcript Polisher
Transform raw podcast or interview transcripts into polished, professional documents that maintain authentic voice while dramatically improving readability.
Purpose
Raw transcripts from automated services are often unreadable - filled with filler words, incomplete sentences, and poor formatting. This skill cleans them up while preserving what was actually said.
Core Philosophy: Balance authenticity with clarity. Remove everything that doesn't add meaning while preserving what was actually said. Create the "ideal version" of what the speaker wanted to communicate - without changing their words or ideas.
Target: 25-35% length reduction while maintaining 100% fidelity to meaning.
When to Use This Skill
- Processing raw transcripts from Rev, Otter, Descript, YouTube auto-captions
- Cleaning up interview recordings or video transcripts
- Preparing spoken content for publication as articles or show notes
- Converting conversational content into readable written format
Not for: Written content that wasn't originally spoken, pre-polished articles, or scripts that are already edited.
Workflow
Step 1: Add Document Structure
Create proper header with:
# [Guest Name]: [Compelling Episode Title]
*[Podcast Name] Episode - [Host Names]*
---
## Timestamped Outline
[Add 10 chapters maximum - see guidelines below]
---
## [Time] Chapter Title
[Content starts here]
Timestamped Outline Rules:
- Format:
**MM:SS** - Descriptive Chapter Title - 10 chapters maximum for 45-60 minute episodes
- Focus on major topic changes, not minute-by-minute
- Use compelling, specific titles (not generic descriptions)
Good Examples:
- ✅ 12:25 - The Turnaround: From Struggling to Thriving
- ✅ 29:08 - Why Successful Strategies Don't Spread
Poor Examples:
- ❌ 12:25 - Guest talks about their experience
- ❌ 29:08 - Discussion about the industry
Step 2: Identify and Label Speakers
Replace generic speaker markers (>>, Speaker 1, etc.) with actual names:
- Bold all speaker names:
**Isaac:** Content here - Use first names for casual podcasts, full names for professional interviews
- Be consistent throughout
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 283 lines · 60 tokens per session scan A 2dda48747ef3
transcript-polisher is a skill published in the GitHub repository cdeistopened/skill-stack (27 stars, last pushed 1mo ago), licensed MIT. It adds 60 tokens to every session and 1,977 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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